{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d9f29b8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b3f92593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "026569d381694adfb3431db27995e19c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from DPF import ShardsDatasetConfig, DatasetReader\n",
    "\n",
    "config = ShardsDatasetConfig.from_path_and_columns(\n",
    "    'example_dataset',\n",
    "    image_name_col='image_name',\n",
    "    text_col=\"caption\"\n",
    ")\n",
    "\n",
    "reader = DatasetReader()\n",
    "processor = reader.read_from_config(config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd80425d",
   "metadata": {},
   "source": [
    "## Print metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d36a4efd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset format: shards\n",
      "Path: example_dataset\n",
      "Modalities: ['image', 'text']\n",
      "Columns: 3\n",
      "Total samples: 500\n"
     ]
    }
   ],
   "source": [
    "processor.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eef9d1b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_path</th>\n",
       "      <th>text</th>\n",
       "      <th>split_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>example_dataset/0.tar/0.jpg</td>\n",
       "      <td>шотландцы в национальной одежде с флагом. - ba...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>example_dataset/0.tar/1.jpg</td>\n",
       "      <td>королевская золотая корона с драгоценностями н...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>example_dataset/0.tar/2.jpg</td>\n",
       "      <td>Пасха</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>example_dataset/0.tar/3.jpg</td>\n",
       "      <td>округа нью-джерси карта печати - elizabeth sto...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>example_dataset/0.tar/4.jpg</td>\n",
       "      <td>английская золотая корона с драгоценностями, и...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>example_dataset/2.tar/495.jpg</td>\n",
       "      <td>bokeh Background</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>example_dataset/2.tar/496.jpg</td>\n",
       "      <td>Healthcare, Medicine,</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>example_dataset/2.tar/497.jpg</td>\n",
       "      <td>A set of business teams icons that include edi...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>example_dataset/2.tar/498.jpg</td>\n",
       "      <td>Vector floral pattern icon collection</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>example_dataset/2.tar/499.jpg</td>\n",
       "      <td>Set of Brown ribbons, banners, badges and labe...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        image_path  \\\n",
       "0      example_dataset/0.tar/0.jpg   \n",
       "1      example_dataset/0.tar/1.jpg   \n",
       "2      example_dataset/0.tar/2.jpg   \n",
       "3      example_dataset/0.tar/3.jpg   \n",
       "4      example_dataset/0.tar/4.jpg   \n",
       "..                             ...   \n",
       "495  example_dataset/2.tar/495.jpg   \n",
       "496  example_dataset/2.tar/496.jpg   \n",
       "497  example_dataset/2.tar/497.jpg   \n",
       "498  example_dataset/2.tar/498.jpg   \n",
       "499  example_dataset/2.tar/499.jpg   \n",
       "\n",
       "                                                  text split_name  \n",
       "0    шотландцы в национальной одежде с флагом. - ba...          0  \n",
       "1    королевская золотая корона с драгоценностями н...          0  \n",
       "2                                                Пасха          0  \n",
       "3    округа нью-джерси карта печати - elizabeth sto...          0  \n",
       "4    английская золотая корона с драгоценностями, и...          0  \n",
       "..                                                 ...        ...  \n",
       "495                                   bokeh Background          2  \n",
       "496                              Healthcare, Medicine,          2  \n",
       "497  A set of business teams icons that include edi...          2  \n",
       "498              Vector floral pattern icon collection          2  \n",
       "499  Set of Brown ribbons, banners, badges and labe...          2  \n",
       "\n",
       "[500 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor.df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f060ae57",
   "metadata": {},
   "source": [
    "## Removing duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "71bd3108",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['image_phash_8']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:01<00:00, 387.02it/s]\n"
     ]
    }
   ],
   "source": [
    "from DPF.filters.images.hash_filters import PHashFilter\n",
    "\n",
    "datafilter = PHashFilter(sim_hash_size=8, workers=16)\n",
    "print(datafilter.result_columns)\n",
    "processor.apply_data_filter(datafilter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a83b48fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "499"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor.filter_df(~processor.df['image_phash_8'].duplicated())\n",
    "len(processor)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6749b74f",
   "metadata": {},
   "source": [
    "## Filter by aesthetic score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "33750661",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['improved_aesthetic_score_ViT-L/14']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10it [00:05,  1.99it/s]                      \n"
     ]
    }
   ],
   "source": [
    "from DPF.filters.images.aesthetic_improved_filter import ImprovedAestheticFilter\n",
    "\n",
    "datafilter = ImprovedAestheticFilter(\n",
    "    weights_folder='../weights',  # path to weights folder, will be downloaded to this folder\n",
    "    device='cuda:0',\n",
    "    workers=16\n",
    ")\n",
    "print(datafilter.result_columns)\n",
    "processor.apply_data_filter(datafilter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "052c03e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_path</th>\n",
       "      <th>text</th>\n",
       "      <th>split_name</th>\n",
       "      <th>image_phash_8</th>\n",
       "      <th>improved_aesthetic_score_ViT-L/14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>example_dataset/0.tar/0.jpg</td>\n",
       "      <td>шотландцы в национальной одежде с флагом. - ba...</td>\n",
       "      <td>0</td>\n",
       "      <td>e1f6e7c0e07f0e3d381f8fc07e09c7f0103fe07038ec7918</td>\n",
       "      <td>5.601146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>example_dataset/0.tar/1.jpg</td>\n",
       "      <td>королевская золотая корона с драгоценностями н...</td>\n",
       "      <td>0</td>\n",
       "      <td>fc3100ffcd0b6301c8ef9f1c0399d8030ebfe38e047db03f</td>\n",
       "      <td>4.966230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>example_dataset/0.tar/2.jpg</td>\n",
       "      <td>Пасха</td>\n",
       "      <td>0</td>\n",
       "      <td>effe38e3be07747080a1d9d8fe2fe80081f8ff8c2701f018</td>\n",
       "      <td>5.068741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>example_dataset/0.tar/3.jpg</td>\n",
       "      <td>округа нью-джерси карта печати - elizabeth sto...</td>\n",
       "      <td>0</td>\n",
       "      <td>ec4007e06e0113b7f8f007e603f63f1381d81f8fff027e33</td>\n",
       "      <td>4.848436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>example_dataset/0.tar/4.jpg</td>\n",
       "      <td>английская золотая корона с драгоценностями, и...</td>\n",
       "      <td>0</td>\n",
       "      <td>e3f1c6ec703ffc01b81b9fc0216e0703efc2fc01b1fa81f0</td>\n",
       "      <td>5.539761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>example_dataset/2.tar/495.jpg</td>\n",
       "      <td>bokeh Background</td>\n",
       "      <td>2</td>\n",
       "      <td>e431028fcd3581f1e6f073071df63df139c303b3d0f23417</td>\n",
       "      <td>5.285481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>example_dataset/2.tar/496.jpg</td>\n",
       "      <td>Healthcare, Medicine,</td>\n",
       "      <td>2</td>\n",
       "      <td>fb8efe188cc61869c6187c6618f9c019ec4618fc771c8ff8</td>\n",
       "      <td>4.501980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>example_dataset/2.tar/497.jpg</td>\n",
       "      <td>A set of business teams icons that include edi...</td>\n",
       "      <td>2</td>\n",
       "      <td>fc70381c70381c71f81c71f81c70385fbe07e38fc7e38fc0</td>\n",
       "      <td>3.883074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>example_dataset/2.tar/498.jpg</td>\n",
       "      <td>Vector floral pattern icon collection</td>\n",
       "      <td>2</td>\n",
       "      <td>e80f7f080d3c0fc13e0c4770f03ec5f38281f7e1bbe0b1c7</td>\n",
       "      <td>5.069400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>example_dataset/2.tar/499.jpg</td>\n",
       "      <td>Set of Brown ribbons, banners, badges and labe...</td>\n",
       "      <td>2</td>\n",
       "      <td>fbf03803f03ce001fc1b8fc103fe073c7e38fc01b8e381df</td>\n",
       "      <td>4.538239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>499 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        image_path  \\\n",
       "0      example_dataset/0.tar/0.jpg   \n",
       "1      example_dataset/0.tar/1.jpg   \n",
       "2      example_dataset/0.tar/2.jpg   \n",
       "3      example_dataset/0.tar/3.jpg   \n",
       "4      example_dataset/0.tar/4.jpg   \n",
       "..                             ...   \n",
       "494  example_dataset/2.tar/495.jpg   \n",
       "495  example_dataset/2.tar/496.jpg   \n",
       "496  example_dataset/2.tar/497.jpg   \n",
       "497  example_dataset/2.tar/498.jpg   \n",
       "498  example_dataset/2.tar/499.jpg   \n",
       "\n",
       "                                                  text split_name  \\\n",
       "0    шотландцы в национальной одежде с флагом. - ba...          0   \n",
       "1    королевская золотая корона с драгоценностями н...          0   \n",
       "2                                                Пасха          0   \n",
       "3    округа нью-джерси карта печати - elizabeth sto...          0   \n",
       "4    английская золотая корона с драгоценностями, и...          0   \n",
       "..                                                 ...        ...   \n",
       "494                                   bokeh Background          2   \n",
       "495                              Healthcare, Medicine,          2   \n",
       "496  A set of business teams icons that include edi...          2   \n",
       "497              Vector floral pattern icon collection          2   \n",
       "498  Set of Brown ribbons, banners, badges and labe...          2   \n",
       "\n",
       "                                        image_phash_8  \\\n",
       "0    e1f6e7c0e07f0e3d381f8fc07e09c7f0103fe07038ec7918   \n",
       "1    fc3100ffcd0b6301c8ef9f1c0399d8030ebfe38e047db03f   \n",
       "2    effe38e3be07747080a1d9d8fe2fe80081f8ff8c2701f018   \n",
       "3    ec4007e06e0113b7f8f007e603f63f1381d81f8fff027e33   \n",
       "4    e3f1c6ec703ffc01b81b9fc0216e0703efc2fc01b1fa81f0   \n",
       "..                                                ...   \n",
       "494  e431028fcd3581f1e6f073071df63df139c303b3d0f23417   \n",
       "495  fb8efe188cc61869c6187c6618f9c019ec4618fc771c8ff8   \n",
       "496  fc70381c70381c71f81c71f81c70385fbe07e38fc7e38fc0   \n",
       "497  e80f7f080d3c0fc13e0c4770f03ec5f38281f7e1bbe0b1c7   \n",
       "498  fbf03803f03ce001fc1b8fc103fe073c7e38fc01b8e381df   \n",
       "\n",
       "     improved_aesthetic_score_ViT-L/14  \n",
       "0                             5.601146  \n",
       "1                             4.966230  \n",
       "2                             5.068741  \n",
       "3                             4.848436  \n",
       "4                             5.539761  \n",
       "..                                 ...  \n",
       "494                           5.285481  \n",
       "495                           4.501980  \n",
       "496                           3.883074  \n",
       "497                           5.069400  \n",
       "498                           4.538239  \n",
       "\n",
       "[499 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5a08f721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "207"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor.filter_df(processor.df['improved_aesthetic_score_ViT-L/14']>5.2)\n",
    "len(processor)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1002a863",
   "metadata": {},
   "source": [
    "## Check samples from filtered dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9f55de06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gift boxes coming out of a yellow megaphone over yellow background. Horizontal composition with copy space. Great use for announcement concepts.\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=612x374>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "m2d, metadata = processor.get_random_sample()\n",
    "print(metadata['text'])\n",
    "Image.open(BytesIO(m2d['image']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f882c6b",
   "metadata": {},
   "source": [
    "## Sharding new dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1a5f9a3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 207/207 [00:01<00:00, 205.79it/s]\n"
     ]
    }
   ],
   "source": [
    "processor.save_to_shards(\n",
    "    'aesthetic_dataset_shards/',\n",
    "    columns_to_save=['text', 'image_phash_8', 'improved_aesthetic_score_ViT-L/14'],\n",
    "    rename_columns={'text': 'caption'},\n",
    "    workers=16\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75bfcb23",
   "metadata": {},
   "source": [
    "## Reading new dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d83c6078",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a73c98333564653ba982726534a7001",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from DPF import ShardsDatasetConfig, DatasetReader\n",
    "\n",
    "config = ShardsDatasetConfig.from_path_and_columns(\n",
    "    'aesthetic_dataset_shards',\n",
    "    image_name_col='image_name',\n",
    "    text_col=\"caption\"\n",
    ")\n",
    "\n",
    "reader = DatasetReader()\n",
    "processor = reader.read_from_config(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bd5ef719",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_path</th>\n",
       "      <th>text</th>\n",
       "      <th>split_name</th>\n",
       "      <th>improved_aesthetic_score_ViT-L/14</th>\n",
       "      <th>image_phash_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aesthetic_dataset_shards/0.tar/0.jpg</td>\n",
       "      <td>шотландцы в национальной одежде с флагом. - ba...</td>\n",
       "      <td>0</td>\n",
       "      <td>5.601146</td>\n",
       "      <td>e1f6e7c0e07f0e3d381f8fc07e09c7f0103fe07038ec7918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aesthetic_dataset_shards/0.tar/1.jpg</td>\n",
       "      <td>фотореальный красный чемодан с городским фоном...</td>\n",
       "      <td>0</td>\n",
       "      <td>5.422980</td>\n",
       "      <td>ff8fc7e0713802703f0dcee7f031180bc7c76078189f84e7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aesthetic_dataset_shards/0.tar/2.jpg</td>\n",
       "      <td>Top view paper bag of different fresh health f...</td>\n",
       "      <td>0</td>\n",
       "      <td>5.558455</td>\n",
       "      <td>e3f0007ff600ec783ffc0fc0fc0bf9fc0dc87f8046807c07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aesthetic_dataset_shards/0.tar/3.jpg</td>\n",
       "      <td>английская золотая корона с драгоценностями, и...</td>\n",
       "      <td>0</td>\n",
       "      <td>5.539761</td>\n",
       "      <td>e3f1c6ec703ffc01b81b9fc0216e0703efc2fc01b1fa81f0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>aesthetic_dataset_shards/0.tar/4.jpg</td>\n",
       "      <td>Помолвка</td>\n",
       "      <td>0</td>\n",
       "      <td>5.234586</td>\n",
       "      <td>e00ec7077e381f803f63f1ff1f78381c033ce3ffc2e07004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>aesthetic_dataset_shards/0.tar/202.jpg</td>\n",
       "      <td>программисты, сотрудничающие в ит-компании, ра...</td>\n",
       "      <td>0</td>\n",
       "      <td>5.353548</td>\n",
       "      <td>e1f1fbe389ff0201df007fc01c0f00f3b800fc7e3f01ce67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>aesthetic_dataset_shards/0.tar/203.jpg</td>\n",
       "      <td>Our new house - Latin family</td>\n",
       "      <td>0</td>\n",
       "      <td>5.283234</td>\n",
       "      <td>e081c0e3f7ffe1783ff21ffcff82017e0380fc403c1c3038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>aesthetic_dataset_shards/0.tar/204.jpg</td>\n",
       "      <td>An urban downtown skyscraper shot at the corne...</td>\n",
       "      <td>0</td>\n",
       "      <td>5.727420</td>\n",
       "      <td>ff8fdcfbf020bc383f020e3af1bfc71c0d406302301c6fc6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>aesthetic_dataset_shards/0.tar/205.jpg</td>\n",
       "      <td>Vector illustration of the infographic elements</td>\n",
       "      <td>0</td>\n",
       "      <td>5.409890</td>\n",
       "      <td>f08ffae137f4d0745aff4008ffc04bf04d83f23da01cc08b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>aesthetic_dataset_shards/0.tar/206.jpg</td>\n",
       "      <td>Aerial overhead view of a multi-ethnic group o...</td>\n",
       "      <td>0</td>\n",
       "      <td>5.466423</td>\n",
       "      <td>e3f180ff81383fff08646147600019f87e3ffc73b8e00e36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>207 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 image_path  \\\n",
       "0      aesthetic_dataset_shards/0.tar/0.jpg   \n",
       "1      aesthetic_dataset_shards/0.tar/1.jpg   \n",
       "2      aesthetic_dataset_shards/0.tar/2.jpg   \n",
       "3      aesthetic_dataset_shards/0.tar/3.jpg   \n",
       "4      aesthetic_dataset_shards/0.tar/4.jpg   \n",
       "..                                      ...   \n",
       "202  aesthetic_dataset_shards/0.tar/202.jpg   \n",
       "203  aesthetic_dataset_shards/0.tar/203.jpg   \n",
       "204  aesthetic_dataset_shards/0.tar/204.jpg   \n",
       "205  aesthetic_dataset_shards/0.tar/205.jpg   \n",
       "206  aesthetic_dataset_shards/0.tar/206.jpg   \n",
       "\n",
       "                                                  text split_name  \\\n",
       "0    шотландцы в национальной одежде с флагом. - ba...          0   \n",
       "1    фотореальный красный чемодан с городским фоном...          0   \n",
       "2    Top view paper bag of different fresh health f...          0   \n",
       "3    английская золотая корона с драгоценностями, и...          0   \n",
       "4                                             Помолвка          0   \n",
       "..                                                 ...        ...   \n",
       "202  программисты, сотрудничающие в ит-компании, ра...          0   \n",
       "203                       Our new house - Latin family          0   \n",
       "204  An urban downtown skyscraper shot at the corne...          0   \n",
       "205    Vector illustration of the infographic elements          0   \n",
       "206  Aerial overhead view of a multi-ethnic group o...          0   \n",
       "\n",
       "     improved_aesthetic_score_ViT-L/14  \\\n",
       "0                             5.601146   \n",
       "1                             5.422980   \n",
       "2                             5.558455   \n",
       "3                             5.539761   \n",
       "4                             5.234586   \n",
       "..                                 ...   \n",
       "202                           5.353548   \n",
       "203                           5.283234   \n",
       "204                           5.727420   \n",
       "205                           5.409890   \n",
       "206                           5.466423   \n",
       "\n",
       "                                        image_phash_8  \n",
       "0    e1f6e7c0e07f0e3d381f8fc07e09c7f0103fe07038ec7918  \n",
       "1    ff8fc7e0713802703f0dcee7f031180bc7c76078189f84e7  \n",
       "2    e3f0007ff600ec783ffc0fc0fc0bf9fc0dc87f8046807c07  \n",
       "3    e3f1c6ec703ffc01b81b9fc0216e0703efc2fc01b1fa81f0  \n",
       "4    e00ec7077e381f803f63f1ff1f78381c033ce3ffc2e07004  \n",
       "..                                                ...  \n",
       "202  e1f1fbe389ff0201df007fc01c0f00f3b800fc7e3f01ce67  \n",
       "203  e081c0e3f7ffe1783ff21ffcff82017e0380fc403c1c3038  \n",
       "204  ff8fdcfbf020bc383f020e3af1bfc71c0d406302301c6fc6  \n",
       "205  f08ffae137f4d0745aff4008ffc04bf04d83f23da01cc08b  \n",
       "206  e3f180ff81383fff08646147600019f87e3ffc73b8e00e36  \n",
       "\n",
       "[207 rows x 5 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd4559d8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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